Linear combinations of mean squares occur in many areas of research like statistical hypothesis testing, design of experiments, and statistical genetics. The present paper demonstrates how the Satterthwaite approximation dating back to early nineteenth century is still a potential tool to reduce complicated problems to a solvable format. Significantly, a new derivation of the distribution of a linear combination of ?Between sires? and ?Between dams within sires? mean squares under certain restrictions is proposed which is quite simple compared the Graybill (1956) approach to the problem. Keywords: Linear combination of mean squares, Satherthwaite approximation.

  Topic modelling has gained prominence in the recent years due to the availability and necessities for the analysis of large volumes of unstructured text data. In agriculture, a huge amount of text data is generated in kisan call centers in the form of queries raised by the farmers. This study attempts to use the Latent Dirichlet Allocation method of topic modelling to discover the hidden topics in the queries raised at kisan call centers of five south Indian states. Through exploratory text analysis, it was found that the most common terms appeared in the query texts are ?weather?, ?management? and ?market?. The topic modelling lead to identification of 12 topics, out of which the topic ?pest management in paddy, cotton and chilli? reported the maximum number of queries. Keywords: Topic models; Latent Dirichlet Allocation; Text analysis; Kisan call center.

  This paper estimates the Technical Efficiency (T.E.) of an apple-producing farmer in Jubbal and Kotkhai tahsil of Shimla district, using a stochastic Cobb-Douglas (CD) production frontier function. A stochastic frontier production function can be used for panel data of firms. The non-negative technical inefficiency effects are assumed to function firm-specific variables and vary over time. They are believed to be independently distributed as truncations of normal distributions with constant variance. The result shows the mean technical efficiency of Jubbal and Kotkhai tahsil Farmer is 66% and 70%. The mean technical efficiency is high for Kotkhai Farmer compared to Jubbal Farmer. Therefore, it is concluded that Kotkhai?s farmers are more technically efficient than Jubbal with the same input. This suggests that Jubbal Farmer can potentially increase their productivity through more efficient use of information. Keywords: Technical efficiency, Stochastic frontier function (SFF), Constraints, Simple size.

  Zonal-yield models incorporating a linear time trend and agro-meteorological (agromet) variables each spanning successive fortnights within the growth period of the cotton crop are developed within the framework of multiple linear regression analysis. These models have been used to predict the cotton yields in four cotton growing districts namely; Hisar, Bhiwani, Sirsa, Fatehabad covering more than 90% of cotton production of the Haryana State. Linear time-trend has been obtained using cotton yield data of the period 1980-81 to 2011-12. The fortnightly weather data along with trend yield have been utilized for the same period for building the zonal weather-yield models. Models have been validated for subsequent years i.e. 2012-13 to 2017-18, not included in the development of the models. The zonal models were fitted by taking DOA yield as dependent variable and fortnightly weather variables along with trend yield/CCT/dummy variables as regressors. The predictive performance(s) of the contending models were observed in terms of average absolute percent deviations of cotton yield forecasts in relation to the observed yield(s) and root mean square error(s). The adequacy of the fitted models was examined through histogram, normal-probability plot for the residuals and residual plot against fitted values for the selected models. Although, the weather variables were found statistically significant as predictors and gave predictions with reasonably high coefficients of determination (R2 ) but the predictions had too high percent deviations to be acceptable and hence were deemed unsuitable for routine crop yield forecasting. To improve the predictive accuracy of the agromet yield models, a dummy regressor variable in the form of Crop Condition Term (CCT), was added to the weather models. The addition of CCT to the weather models significantly improved the accuracies of the district-level yield predictions in the State. The predictive performance of the zonal agromet models was assessed using multiple metrics, including the adjusted R2 , the percent deviations of the forecast yields from the Department of Agriculture (DOA) yield estimates and the root mean square errors (RMSEs). Keywords: Maximum temperature, Minimum temperature, Rainfall, Sun shine hours and relative humidity, Trend yield, Crop condition term.

  A new family of bimodal distributions is introduced in this paper with an objective of using them for modelling error data sets. A new class of statistics arising from a symmetric distribution is proved to have distributions belonging to the family of the bimodal distributions introduced in this work. The information matrix is derived after addressing the problem of obtaining maximum-likelihood estimates for the parameters of generalized bimodal distribution. A simulation study is conducted to evaluate the properties of maximum likelihood estimators. The applications of the results in building bimodal distributions for some real life data sets are also illustrated. Keywords: Error data; Symmetric distributions; Bimodal distributions; Maximum likelihood estimate; Ordered density value induced statistics.